Improving customer support is a quick win for delivering short-term ROI from LLMs and AI search capabilities. LLMs require centralizing an enterprise’s unstructured data, including data embedded in CRMs, file systems, and other SaaS tools. Once IT centralizes this data and implements a private LLM, other opportunities include improving sales lead conversion and HR onboarding processes.
“Companies have been stuffing data into SharePoint and other systems for decades,” says Gordon Allott, president and CEO of GetK3. “It might actually be worth something by cleaning it up and using an LLM.”
Mitigate risks by communicating an LLM governance model
The generative AI landscape has more than 100 tools covering test, image, video, code, speech, and other categories. What stops employees from trying a tool and pasting proprietary or other confidential information into their prompts?
Rodenbostel suggests, “Leaders must ensure their teams only use these tools in approved, appropriate ways by researching and creating an acceptable use policy.”
There are three departments where CIOs must partner with their CHROs and CISOs in communicating policy and creating a governance model that supports smart experimentation. First, CIOs should evaluate how ChatGPT and other generative AIs impact coding and software development. IT must lead by example on where and how to experiment and when not to use a tool or proprietary data set.
Marketing is the second area to focus on, where marketers can use ChatGPT and other generative AIs in content creation, lead generation, email marketing, and over ten common marketing practices. With more than 11,000 marketing technology solutions available today, there are plenty of opportunities to experiment and make inadvertent mistakes in testing SaaS with new LLM capabilities.
CIOs of leading organizations are creating a registry to onboard new generative AI use cases, define a process for reviewing methodologies, and centralize capturing the impact of AI experiments.
Re-evaluate decision-making processes and authorities
One important area to consider is how generative AI will impact decision-making processes and the future of work.
Over the past decade, many businesses have aimed to become data-driven organizations by democratizing access to data, training more businesspeople on citizen data science, and instilling proactive data governance practices. Generative AI unleashes new capabilities, enabling leaders to prompt and get quick answers, but timeliness, accuracy, and bias are key issues for many LLMs.
“Keeping humans at the center of AI and establishing robust frameworks for data usage and model interpretability will go a long way in mitigating bias within these models and ensuring all AI outputs are ethical and responsible,” says Erik Voight, VP of enterprise solutions of Appen. “The reality is that AI models are no replacement for humans when it comes to critical decision-making and should be used to supplement these processes, not take them over entirely.”
CIOs should seek a balanced approach to prioritizing generative AI initiatives, including defining governance, identifying short-term efficiencies, and seeking longer-term transformation opportunities.
